OpenRaiser/Intern-Atlas huggingface dataset:
White Paper on Business of 6G
Venue: arXiv cs.OH 2020
Year: 2020

Developing products, services and vertical applications for the future digitized society in the 6G era requires a multidisciplinary approach and a re-definition of how we create, deliver and consume network resources, data and services for both communications and sensing purposes. This development will change and disrupt the traditional business models and ecosystem roles of digital service providers, as well as open the market for key stakeholders in the 6G era like digital service operators, cloud operators and resource brokers. White paper discusses unprecedented opportunities of enabling and empowering multiple stakeholders to have a more active participation in the future 6G ecosystem via novel sustainable open ecosystemic business models with flexible integration of long tail services with tailored performance attributes. This research adopts a qualitative scenario planning method and portrays three scenario themes resulting in a total of 12 scenarios for the futures of the 6G business. By focusing on key trends, their interactions, and irreducible uncertainties, scenario building generates perspectives for the futures within which alternative 6G business strategies were developed and assessed for a traditional incumbent mobile network operator and a novel 6G digital service provider stemming from redefined sustainable economics. Value-capture in the 6G era requires understanding the dynamics of platforms and ecosystems. Results indicate that, to reach some of the preferred futures, we should pay attention to the privacy and security issues related to business and regulation needs; public/governmental, corporate, community and user(s) perspectives to and aims of governance; ecosystem configuration related to users, decentralized business models and platforms; user empowerment; and the role of location-specificity of services.
FaR-GAN for One-Shot Face Reenactment
Venue: arXiv cs.CV 2020
Year: 2020

Animating a static face image with target facial expressions and movements is important in the area of image editing and movie production. This face reenactment process is challenging due to the complex geometry and movement of human faces. Previous work usually requires a large set of images from the same person to model the appearance. In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the same source identity but with the target expression. The proposed method makes no assumptions about the source identity, facial expression, head pose, or even image background. We evaluate our method on the VoxCeleb1 dataset and show that our method is able to generate a higher quality face image than the compared methods.
ODVICE: An Ontology-Driven Visual Analytic Tool for Interactive Cohort Extraction
Venue: arXiv cs.LG 2020
Year: 2020

Increased availability of electronic health records (EHR) has enabled researchers to study various medical questions. Cohort selection for the hypothesis under investigation is one of the main consideration for EHR analysis. For uncommon diseases, cohorts extracted from EHRs contain very limited number of records - hampering the robustness of any analysis. Data augmentation methods have been successfully applied in other domains to address this issue mainly using simulated records. In this paper, we present ODVICE, a data augmentation framework that leverages the medical concept ontology to systematically augment records using a novel ontologically guided Monte-Carlo graph spanning algorithm. The tool allows end users to specify a small set of interactive controls to control the augmentation process. We analyze the importance of ODVICE by conducting studies on MIMIC-III dataset for two learning tasks. Our results demonstrate the predictive performance of ODVICE augmented cohorts, showing ~30% improvement in area under the curve (AUC) over the non-augmented dataset and other data augmentation strategies.
Pika parsing: reformulating packrat parsing as a dynamic programming algorithm solves the left recursion and error recovery problems
Venue: arXiv cs.PL 2020
Year: 2020

A recursive descent parser is built from a set of mutually-recursive functions, where each function directly implements one of the nonterminals of a grammar. A packrat parser uses memoization to reduce the time complexity for recursive descent parsing from exponential to linear in the length of the input. Recursive descent parsers are extremely simple to write, but suffer from two significant problems: (i) left-recursive grammars cause the parser to get stuck in infinite recursion, and (ii) it can be difficult or impossible to optimally recover the parse state and continue parsing after a syntax error. Both problems are solved by the pika parser, a novel reformulation of packrat parsing as a dynamic programming algorithm, which requires parsing the input in reverse: bottom-up and right to left, rather than top-down and left to right. This reversed parsing order enables pika parsers to handle grammars that use either direct or indirect left recursion to achieve left associativity, simplifying grammar writing, and also enables optimal recovery from syntax errors, which is a crucial property for IDEs and compilers. Pika parsing maintains the linear-time performance characteristics of packrat parsing as a function of input length. The pika parser was benchmarked against the widely-used Parboiled2 and ANTLR4 parsing libraries. The pika parser performed significantly better than the other parsers for an expression grammar, although for a complex grammar implementing the Java language specification, a large constant performance impact was incurred per input character. Therefore, if performance is important, pika parsing is best applied to simple to moderate-sized grammars, or to very large inputs, if other parsing alternatives do not scale linearly in the length of the input. Several new insights into precedence, associativity, and left recursion are presented.
FlowCFL: A Framework for Type-based Reachability Analysis in the Presence of Mutable Data
Venue: arXiv cs.PL 2020
Year: 2020

Reachability analysis is a fundamental program analysis with a wide variety of applications. We present FlowCFL, a framework for type-based reachability analysis in the presence of mutable data. Interestingly, the underlying semantics of FlowCFL is CFL-reachability. We make three contributions. First, we define a dynamic semantics that captures the notion of flow commonly used in reachability analysis. Second, we establish correctness of CFL-reachability over graphs with inverse edges (inverse edges are necessary for the handling of mutable heap data). Our approach combines CFL-reachability with reference immutability to avoid the addition of certain infeasible inverse edges and we demonstrate empirically that avoiding those edges results in precision improvement. Our formal account of correctness extends to this case as well. Third, we present a type-based reachability analysis and establish equivalence between a certain CFL-reachability analysis and the type-based analysis, thus proving correctness of the type-based analysis.
Genome-Wide Epigenetic Modifications as a Shared Memory Consensus Problem
Venue: arXiv cs.DC 2020
Year: 2020

A distributed computing system is a collection of processors that communicate either by reading and writing from a shared memory or by sending messages over some communication network. Most prior biologically inspired distributed computing algorithms rely on message passing as the communication model. Here we show that in the process of genome-wide epigenetic modifications cells utilize their DNA as a shared memory system. We formulate a particular consensus problem, called the epigenetic consensus problem, that cells attempt to solve using this shared memory model, and then present algorithms, derive expected run time and discuss, analyze and simulate improved methods for solving this problem. Analysis of real biological data indicates that the computational methods indeed reflect aspects of the biological process for genome-wide epigenetic modifications.
Generating collection transformations from proofs
Venue: arXiv cs.LO 2020
Year: 2020

Nested relations, built up from atomic types via product and set types, form a rich data model. Over the last decades the nested relational calculus, NRC, has emerged as a standard language for defining transformations on nested collections. NRC is a strongly-typed functional language which allows building up transformations using tupling and projections, a singleton-former, and a map operation that lifts transformations on tuples to transformations on sets. In this work we describe an alternative declarative method of describing transformations in logic. A formula with distinguished inputs and outputs gives an implicit definition if one can prove that for each input there is only one output that satisfies it. Our main result shows that one can synthesize transformations from proofs that a formula provides an implicit definition, where the proof is in an intuitionistic calculus that captures a natural style of reasoning about nested collections. Our polynomial time synthesis procedure is based on an analog of Craig's interpolation lemma, starting with a provable containment between terms representing nested collections and generating an NRC expression that interpolates between them. We further show that NRC expressions that implement an implicit definition can be found when there is a classical proof of functionality, not just when there is an intuitionistic one. That is, whenever a formula implicitly defines a transformation, there is an NRC expression that implements it.
Towards Automatic building of Human-Machine Conversational System to support Maintenance Processes
Venue: arXiv cs.HC 2020
Year: 2020

Companies are dealing with many cognitive changes with the introduction of the Industry 4.0 paradigm. In this constantly changing environment, knowledge management is a key factor. Dialog systems, being able to hold a conversation with humans, could support the knowledge management in business environment. Although, these systems are currently hand-coded and need the intervention of a human being in writing all the possible questions and answers, and then planning the interactions. This process, besides being time-consuming, is not scalable. Conversely, a dialog system, also referred to as chatbot, can be built from scratch by simply extracting rules from technical documentation. So, the goal of this research is designing a methodology for automatic building of human-machine conversational system, able to interact in an industrial environment. An initial taxonomy, containing entities expected to be found in maintenance manuals, is used to identify the relevant sentences of a manual provided by the company BOBST SA and applying text mining techniques, it is automatically expanded. The final result is a taxonomy network representing the entities and their relation, that will be used in future works for managing the interactions of a maintenance chatbot.
Deep Learning for Political Science
Venue: arXiv cs.CL 2020
Year: 2020

Political science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way. Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research. This chapter offers an introduction to such methods drawing examples from political science. Focusing on the areas where the strengths of the methods coincide with challenges in these fields, the chapter first presents an introduction to AI and its core technology - machine learning, with its rapidly developing subfield of deep learning. The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts.
Neutrality May Matter: Sentiment Analysis in Reviews of Airbnb, Booking, and Couchsurfing in Brazil and USA
Venue: arXiv cs.SI 2020
Year: 2020

Information and communications technologies have enabled the rise of the phenomenon named sharing economy, which represents activities between people, coordinated by online platforms, to obtain, provide, or share access to goods and services. In hosting services of the sharing economy, it is common to have a personal contact between the host and guest, and this may affect users' decision to do negative reviews, as negative reviews can damage the offered services. To evaluate this issue, we collected reviews from two sharing economy platforms, Airbnb and Couchsurfing, and from one platform that works mostly with hotels (traditional economy), Booking.com, for some cities in Brazil and the USA. Trough a sentiment analysis, we found that reviews in the sharing economy tend to be considerably more positive than those in the traditional economy. This can represent a problem in those systems, as an experiment with volunteers performed in this study suggests. In addition, we discuss how to exploit the results obtained to help improve users' decision making.
Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
Venue: arXiv cs.CL 2020
Year: 2020

The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been categorized to a certain class. In order to address this problem, we propose a step-wise disease Named Entity Recognition (DNER) extraction and PICO identification method. With our method, sentences in paper title and abstract are first classified into different classes of PICO, and medical entities are then identified and classified into P and O. Different kinds of deep learning frameworks are used and experimental results show that our method will achieve high performance and fine-grained extraction results comparing with conventional PICO extraction works.
Essential requirements for establishing and operating data trusts: practical guidance based on a working meeting of fifteen Canadian organizations and initiatives
Venue: arXiv cs.CY 2020
Year: 2020

Introduction: Increasingly, the label data trust is being applied to repeatable mechanisms or approaches to sharing data in a timely, fair, safe and equitable way. However, there is a gap in terms of practical guidance about how to establish and operate a data trust. Aim and Approach: In December 2019, the Canadian Institute for Health Information and the Vector Institute for Artificial Intelligence convened a working meeting of 19 people representing 15 Canadian organizations/initiatives involved in data sharing, most of which focus on public sector health data. The objective was to identify essential requirements for the establishment and operation of data trusts. Preliminary findings were presented during the meeting then refined as participants and co-authors identified relevant literature and contributed to this manuscript. Results: Twelve (12) minimum specification requirements (min specs) for data trusts were identified. The foundational min spec is that data trusts must meet all legal requirements, including legal authority to collect, hold or share data. In addition, there was agreement that data trusts must have (i) an accountable governing body which ensures the data trust advances its stated purpose and is transparent, (ii) comprehensive data management including responsible parties and clear processes for the collection, storage, access, disclosure and use of data, (iii) training and accountability requirements for all data users and (iv) ongoing public and stakeholder engagement. Conclusion / Implications: Based on a review of the literature and advice from participants from 15 Canadian organizations/initiatives, practical guidance in the form of twelve min specs for data trusts were agreed on. Public engagement and continued exchange of insights and experience is recommended on this evolving topic.
Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing?
Venue: arXiv cs.CY 2020
Year: 2020

Many digital solutions mainly involving Bluetooth technology are being proposed for Contact Tracing Apps (CTA) to reduce the spread of COVID-19. Concerns have been raised regarding privacy, consent, uptake required in a given population, and the degree to which use of CTAs can impact individual behaviours. However, very few groups have taken a holistic approach and presented a combined solution. None has presented their CTA in such a way as to ensure that even the most suggestible member of our community does not become complacent and assume that CTA operates as an invisible shield, making us and our families impenetrable or immune to the disease. We propose to build on some of the digital solutions already under development that, with addition of a Bayesian model that predicts likelihood for infection supplemented by traditional symptom and contact tracing, that can enable us to reach 90% of a population. When combined with an effective communication strategy and social distancing, we believe solutions like the one proposed here can have a very beneficial effect on containing the spread of this pandemic.
Patient Similarity Analysis with Longitudinal Health Data
Venue: arXiv cs.LG 2020
Year: 2020

Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.
A Rate-Distortion view of human pragmatic reasoning
Venue: arXiv cs.CL 2020
Year: 2020

What computational principles underlie human pragmatic reasoning? A prominent approach to pragmatics is the Rational Speech Act (RSA) framework, which formulates pragmatic reasoning as probabilistic speakers and listeners recursively reasoning about each other. While RSA enjoys broad empirical support, it is not yet clear whether the dynamics of such recursive reasoning may be governed by a general optimization principle. Here, we present a novel analysis of the RSA framework that addresses this question. First, we show that RSA recursion implements an alternating maximization for optimizing a tradeoff between expected utility and communicative effort. On that basis, we study the dynamics of RSA recursion and disconfirm the conjecture that expected utility is guaranteed to improve with recursion depth. Second, we show that RSA can be grounded in Rate-Distortion theory, while maintaining a similar ability to account for human behavior and avoiding a bias of RSA toward random utterance production. This work furthers the mathematical understanding of RSA models, and suggests that general information-theoretic principles may give rise to human pragmatic reasoning.
Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network
Venue: arXiv cs.CV 2020
Year: 2020

We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings. We show that our model outperforms the current state of the art in learning a single enhancement mapping, while having significantly fewer parameters than its competitors. Furthermore, the model achieves even higher performance on learning multiple mappings simultaneously, by taking advantage of shared representations. Our network is based on the recently proposed SGN architecture, with modifications targeted at incorporating global features and style adaption. Finally, we present an unpaired learning method for multitask image enhancement, that is based on generative adversarial networks (GANs).
The Extended Theory of Trees and Algebraic (Co)datatypes
Venue: arXiv cs.LO 2020
Year: 2020

The first-order theory of finite and infinite trees has been studied since the eighties, especially by the logic programming community. Following Djelloul, Dao and Frühwirth, we consider an extension of this theory with an additional predicate for finiteness of trees, which is useful for expressing properties about (not just datatypes but also) codatatypes. Based on their work, we present a simplification procedure that determines whether any given (not necessarily closed) formula is satisfiable, returning a simplified formula which enables one to read off all possible models. Our extension makes the algorithm usable for algebraic (co)datatypes, which was impossible in their original work due to restrictive assumptions. We also provide a prototype implementation of our simplification procedure and evaluate it on instances from the SMT-LIB.
Scaling Blockchains Without Giving up Decentralization and Security
Venue: arXiv cs.DC 2020
Year: 2020

Public blockchains should be able to scale with respect to the number of nodes and to the transactions workload. The blockchain scalability trilemma has been informally conjectured. This is related to scalability, security and decentralization, stating that any improvement in one of these aspects should negatively impact on at least one of the other twos. In fact, despite the large research and experimental effort, all known approaches turn out to be tradeoffs. We theoretically describe a new blockchain architecture that scales to arbitrarily high workload provided that a corresponding proportional increment of nodes is provisioned. We show that, under reasonable assumptions, our approach does not require tradeoffs on security or decentralization. To the best of our knowledge, this is the first result that disprove the trilemma considering the scalability of all architectural elements of a blockchain and not only the consensus protocol. While our result is currently only theoretic, we believe that ot our approach may stimulate significant practical contributions.